Efficiency and sustainability assessment for a group of farmers in the Brazilian Amazon

The aim of this paper is to use DEA models to evaluate sustainability in agriculture. Several variables are taken into account and the resulting efficiency is measured by comparison. The performance of family farms is analysed here (variables: farmed area, work force, and production). As agricultural sustainability depends on the maintenance of systems of production for long periods of time, the models were run for the years of 1986 and 2002. Tiered DEA models were used to group farmers in sustainability categories. Non-parametric regression models were used to identify the factors affecting the efficiency measurements. All the results indicate that the majority of the farmers increased their efficiency along the time. These improvements may support the existence of sustainability.     

Multicategory classification via discrete support vector machines

Discrete support vector machines (DSVM), originally proposed for binary classification problems, have been shown to outperform other competing approaches on well-known benchmark datasets. Here we address their extension to multicategory classification, by developing three different methods. Two of them are based respectively on one-against-all and round-robin classification schemes, in which a number of binary discrimination problems are solved by means of a variant of DSVM. The third method directly addresses the multicategory classification task, by building a decision tree in which an optimal split to separate classes is derived at each node by a new extended formulation of DSVM. Computational tests on publicly available datasets are then conducted to compare the three multicategory classifiers based on DSVM with other methods, indicating that the proposed techniques achieve significantly higher accuracies. This research was partially supported by PRIN grant 2004132117.     

Measuring the performance of manufacturing firms with super slacks based model of data envelopment analysis: An application of 500 major industrial enterprises in Turkey

Data envelopment analysis (DEA) is a mathematical programming technique which has a wide application area. There are many applications of DEA to measure firms’ performance. Balance sheet data is frequently used in order to measure performance of firms through DEA. So it is the characteristic of balance sheets that assets and liabilities amount to the same value. When the data for inputs and outputs are selected from both assets and liabilities sections of the balance sheet, it is important that more attention be paid to the analysis since values assigned to inputs and outputs could be included in assets and liabilities at the same time. Such a situation could create problems concerning the conclusions drawn as a result of analysis.     

Reformulation of the support set selection problem in the logical analysis of data

The paper is concerned with the problem of binary classification of data records, given an already classified training set of records. Among the various approaches to the problem, the methodology of the logical analysis of data (LAD) is considered. Such approach is based on discrete mathematics, with special emphasis on Boolean functions. With respect to the standard LAD procedure, enhancements based on probability considerations are presented. In particular, the problem of the selection of the optimal support set is formulated as a weighted set covering problem. Testable statistical hypothesis are used. Accuracy of the modified LAD procedure is compared to that of the standard LAD procedure on datasets of the UCI repository. Encouraging results are obtained and discussed.     

新型双通道Mach-Zehnder干涉仪多普勒测风激光雷达鉴频系统研究及仿真

Mach-Zehnder(M-Z)干涉仪可作为鉴频器件应用于多普勒测风激光雷达系统中.鉴于一般M-Z干涉仪的稳定性差,不易于调节的缺点,提出一种基于双棱镜结构的新型双通道M-Z干涉仪作为多普勒测风激光雷达鉴频器件.在进行探测原理分析的基础上,利用光学设计软件对其鉴频系统结构进行了参量优化设计和系统仿真.通过设定实验参量并进行光线追迹模拟仿真实验结果,应用反演理论获得了风速值.利用多普勒频移公式计算获得理论风速并与仿真结果进行了对比,结果表明反演仿真风速与理论风速值基本吻合,标准差为0.46m/s.此新型双通道M-Z干涉仪可以作为鉴频器件应用于多普勒测风激光雷达系统中,在光路的调节及提高系统稳定性上具有优势.     

基于DCGAN的拉曼光谱样本扩充及应用研究

拉曼光谱检测方法依赖于化学计量学算法,深度学习是当下最炙手可热的方向,可应用于拉曼光谱进行建模.但是深度学习需要大样本进行训练,而拉曼光谱采集受制于器材和人力成本,获取大批量的样本需要更大成本,且易受荧光等因素干扰,这些问题都制约了将深度学习应用于拉曼光谱.针对以上问题,通过引入深度卷积生成对抗网络(DCGAN)提取拉...     

在SDSS DR8恒星光谱中自动搜寻稀有天体

SDSS DR8海量光谱中包含许多有研究价值的稀有天体,如特殊白矮星(DZ,DQ,DC)、碳星、白矮主序双星、激变变星等,如何在海量光谱中自动搜寻稀有天体有着极其重要的意义。提出一种基于核密度估计和K-近邻(K-nearest neighbor, KNN)相结合的方法在SDSS DR8 信噪比大于5的546 383个恒星光谱中搜寻稀有天体。首先对光谱进行高斯核密度估计,选取概率最小的5 000个光谱作为稀有类,概率最大的300 000个光谱作为普通类,然后进行KNN分类,同时也将5 000个稀有光谱的K个最近邻也作为稀有的天体,结果共有21 193条光谱。为了方便分析,对这些光谱聚类后进行人工检查。这些光谱主要包括由于数据缺失、红化、流量定标不准引起的问题光谱、行星状星云、没有物理联系的光谱双星、类星体、特殊白矮星(DZ,DQ,DC)、碳星、白矮主序双星、激变变星等。通过和SIMBAD,NED,ADS及一些主要的文献交叉验证,我们新发现了3个DZ白矮星、1个白矮主序双星、2个伴星为G型星的激变变星,3个激变变星的候选体、6个DC白矮星,1个DC白矮星候选体和1个 BL Lacertae(BL lac)候选体。还发现了1个有CaⅡ三重发射线和MgⅠ发射线的DA白矮星和1个光谱上表现出发射线的晚M恒星但测光图上像是一个星云或星系。     

便携式X射线荧光光谱法与原子吸收/原子荧光法测定土壤重金属的对比研究

应用便携式X射线荧光光谱仪(PXRF)分别在原位和实验室条件下对53个土壤样品中的Cu,Pb,As,Cr,Ni和Zn等重金属进行测定,并与原子吸收/原子荧光法测定值进行对比,建立一元线性回归模型分析PXRF数据质量。通过测定土壤样品原位含水量并选取部分样品进行室内水分定量实验,分析土壤水分对于PXRF测定结果的影响。结果表明,PXRF检出限分别为Cu: 10.6 mg·kg-1,Pb: 8.1 mg·kg-1,As: 5.7 mg·kg-1,Cr: 22.5 mg·kg-1,Ni: 21.6 mg·kg-1,Zn: 10.4 mg·kg-1;原位测定时Pb,Cr,Ni和Zn可以达到定量水平;经过风干磨细处理,Cu,Pb,Cr,Ni和Zn在实验室条件下可以达到定量水平,说明X射线荧光光谱法适用于土壤重金属的快速测定与评价。水分对于PXRF测定结果具有“稀释”作用,原位条件下土壤含水量<15%时与>25%时样品的平均相对误差分别为-17%与-31%;实验室条件下土壤含水量从风干土水平提高到30%,测定的平均相对误差由10%变为-24%。土壤水分升高可能会导致数据质量和准确性降低,建议原位测定时控制土壤含水量在25%以内。     

Mining axiomatic fuzzy set association rules for classification problems

In this paper, we propose a novel method to mine association rules for classification problems namely AFSRC (AFS association rules for classification) realized in the framework of the axiomatic fuzzy set (AFS) theory. This model provides a simple and efficient rule generation mechanism. It can also retain meaningful rules for imbalanced classes by fuzzifying the concept of the class support of a rule. In addition, AFSRC can handle different data types occurring simultaneously. Furthermore, the new model can produce membership functions automatically by processing available data. An extensive suite of experiments are reported which offer a comprehensive comparison of the performance of the method with the performance of some other methods available in the literature. The experimental result shows that AFSRC outperforms most of other methods when being quantified in terms of accuracy and interpretability. AFSRC forms a classifier with high accuracy and more interpretable rule base of smaller size while retaining a sound balance between these two characteristics.     

Gaining insight into student satisfaction using comprehensible data mining techniques

As a consequence of the heightened competition on the education market, the management of educational institutions often attempts to collect information on what drives student satisfaction by e.g. organizing large scale surveys amongst the student population. Until now, this source of potentially very valuable information remains largely untapped. In this study, we address this issue by investigating the applicability of different data mining techniques to identify the main drivers of student satisfaction in two business education institutions. In the end, the resulting models are to be used by the management to support the strategic decision making process. Hence, the aspect of model comprehensibility is considered to be at least equally important as model performance. It is found that data mining techniques are able to select a surprisingly small number of constructs that require attention in order to manage student satisfaction.